Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	120,509
2	South Dakota	110,955
3	Iowa	88,210
4	Wisconsin	88,117
5	Nebraska	84,883
6	Utah	84,007
7	Rhode Island	81,925
8	Tennessee	81,642
9	Idaho	77,730
10	Wyoming	75,891
11	Montana	75,334
12	Kansas	75,314
13	Illinois	74,979
14	Indiana	74,751
15	Minnesota	72,907
16	Arkansas	72,650
17	Alabama	71,750
18	Oklahoma	71,716
19	Nevada	71,551
20	Mississippi	70,571
21	Arizona	70,142
22	Missouri	66,857
23	New Mexico	66,723
24	Louisiana	65,497
25	Alaska	62,683
26	Florida	60,166
27	Texas	59,863
28	Kentucky	59,124
29	Georgia	58,761
30	Ohio	58,393
31	South Carolina	58,383
32	Delaware	57,702
33	Colorado	57,324
34	California	56,770
35	New Jersey	52,866
36	Massachusetts	52,387
37	Michigan	52,377
38	Connecticut	51,038
39	North Carolina	50,194
40	Pennsylvania	49,061
41	New York	48,871
42	West Virginia	46,186
43	Maryland	44,869
44	District of Columbia	40,432
45	Virginia	39,868
46	Puerto Rico	34,256
47	Washington	32,264
48	New Hampshire	31,401
49	Oregon	26,378
50	Maine	17,042
51	Hawaii	14,958
52	Vermont	11,541

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Rhode Island	1,486
2	California	957
3	Connecticut	862
4	Arizona	790
5	Texas	769
6	South Carolina	758
7	Kansas	723
8	New Hampshire	677
9	Oklahoma	612
10	Delaware	587
11	Arkansas	582
12	Louisiana	572
13	Alabama	567
14	Idaho	563
15	Mississippi	561
16	Massachusetts	555
17	Tennessee	551
18	Utah	545
19	Georgia	526
20	New York	523
21	Ohio	510
22	Kentucky	499
23	West Virginia	482
24	Pennsylvania	479
25	Nebraska	451
26	South Dakota	450
27	Wyoming	447
28	Nevada	439
29	Missouri	433
30	Florida	429
31	New Mexico	426
32	Virginia	418
33	Indiana	409
34	Wisconsin	402
35	District of Columbia	389
36	Illinois	375
37	North Carolina	372
38	Iowa	371
39	Puerto Rico	349
40	Maine	337
41	New Jersey	327
42	Maryland	312
43	Montana	310
44	Colorado	303
45	Alaska	277
46	Minnesota	269
47	Michigan	253
48	Oregon	229
49	Washington	215
50	North Dakota	207
51	Vermont	126
52	Hawaii	49

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,114
2	New York	1,915
3	Massachusetts	1,772
4	North Dakota	1,683
5	Connecticut	1,661
6	Rhode Island	1,661
7	South Dakota	1,634
8	Louisiana	1,591
9	Mississippi	1,585
10	Illinois	1,389
11	Michigan	1,297
12	Iowa	1,208
13	Pennsylvania	1,202
14	Indiana	1,195
15	Arkansas	1,193
16	Arizona	1,187
17	New Mexico	1,146
18	District of Columbia	1,098
19	South Carolina	1,009
20	Florida	996
21	Nevada	985
22	Georgia	983
23	Tennessee	973
24	Alabama	966
25	Maryland	959
26	Texas	948
27	Missouri	939
28	Minnesota	931
29	Delaware	922
30	Wisconsin	885
31	Montana	878
32	Kansas	874
33	Nebraska	842
34	Colorado	826
35	Idaho	781
36	Ohio	746
37	West Virginia	716
38	Wyoming	699
39	Kentucky	649
40	North Carolina	632
41	California	631
42	Oklahoma	607
43	Virginia	576
44	New Hampshire	540
45	Puerto Rico	457
46	Washington	450
47	Utah	386
48	Oregon	345
49	Alaska	263
50	Maine	247
51	Vermont	208
52	Hawaii	199

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Wyoming	18
2	Arkansas	17
3	Mississippi	17
4	Rhode Island	17
5	New Mexico	13
6	Connecticut	12
7	Indiana	12
8	Pennsylvania	12
9	Tennessee	12
10	Arizona	10
11	Illinois	10
12	Massachusetts	10
13	Michigan	9
14	Nevada	9
15	Louisiana	8
16	New Hampshire	8
17	Idaho	7
18	Iowa	7
19	Nebraska	7
20	Ohio	7
21	Wisconsin	7
22	California	6
23	Colorado	6
24	District of Columbia	6
25	Kentucky	6
26	Maryland	6
27	Missouri	6
28	Montana	6
29	New Jersey	6
30	New York	6
31	Washington	6
32	Minnesota	5
33	North Dakota	5
34	Texas	5
35	West Virginia	5
36	Florida	4
37	Kansas	4
38	Oklahoma	4
39	South Carolina	4
40	Vermont	4
41	Alabama	3
42	Maine	3
43	Virginia	3
44	Georgia	2
45	Oregon	2
46	Puerto Rico	2
47	Utah	2
48	Delaware	1
49	North Carolina	1
50	Alaska	0
51	Hawaii	0
52	South Dakota	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	270,252	1	99
Norton	Kansas	219,922	2	99
Lincoln	Arkansas	215,372	3	99
Dewey	South Dakota	213,510	4	99
Bon Homme	South Dakota	213,302	5	99
Davidson	Tennessee	96,927	323	89
Richland	South Carolina	62,065	1581	49
York	South Carolina	52,737	2053	34
Orange	California	50,533	2146	31
Pierce	Washington	29,994	2826	10

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	7,587	1	99
Jerauld	South Dakota	7,452	2	99
Dickey	North Dakota	6,568	3	99
Gregory	South Dakota	6,213	4	99
Foster	North Dakota	5,919	5	99
Davidson	Tennessee	801	1800	42
Richland	South Carolina	801	1801	42
York	South Carolina	634	2109	32
Orange	California	582	2201	29
Pierce	Washington	372	2578	17

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.